The latest articles on DEV Community by Harshit Aggarwal (@harshit_aggarwal_2e641193). https://dev.to/harshit_aggarwal_2e641193 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2746737%2F98231415-9745-48c0-a61f-745b2c309736.jpg https://dev.to/harshit_aggarwal_2e641193 en Harshit Aggarwal Fri, 05 Dec 2025 21:49:16 +0000 https://dev.to/harshit_aggarwal_2e641193/from-chaos-to-code-alphalabs-103e https://dev.to/harshit_aggarwal_2e641193/from-chaos-to-code-alphalabs-103e <h2> The Problem That Kept Me Up at Night </h2> <p>I wanted to build a platform where anyone could create AI trading agents, backtest strategies, and prove their performance. The challenge was coordinating AI models, real-time market data, WebSocket streams, and financial calculations.</p> <p>Discovering Kiro’s specs feature changed everything.</p> <h2> The Inspiration: nof1.ai Meets Algorithmic Trading </h2> <p>Traditional algorithmic trading depends on rigid rules tied to a single trading point.</p> <p>AI trading is different. Instead of predefined conditions, AI can:</p> <ul> <li>Analyze multiple indicators</li> <li>Consider broader market context</li> <li>Adapt to incoming data</li> <li>Make nuanced decisions beyond binary checks</li> </ul> <p>AlphaLabs allows users to provide tickers, indicators, and context. The AI uses holistic reasoning rather than single-condition logic.</p> <h2> Why Kiro Was My Secret Weapon </h2> <h3> 1. Specs: The Blueprint That Saved Me </h3> <p>The <code>.kiro/specs</code> directory became the backbone of the project.</p> <p>Specs covered:</p> <ul> <li>Backend architecture</li> <li>Trading engine</li> <li>FastAPI migration</li> <li>Code quality improvements</li> <li>Custom indicator engine</li> </ul> <p>Each spec included:</p> <ul> <li>Requirements </li> <li>Design </li> <li>Implementation tasks </li> </ul> <p>This enabled structured development, progress tracking, and consistent architecture.</p> <h3> 2. The Frankenstein Architecture </h3> <p>AlphaLabs combines several components that rarely coexist in one system.</p> <h4> AI + Trading Logic </h4> <ul> <li>OpenRouter integration with multiple AI models </li> <li>JSON trading decisions (LONG, SHORT, HOLD, CLOSE) </li> <li>Contextual decision-making </li> <li>Retry logic, timeouts, circuit breakers </li> <li>Fallback to HOLD </li> </ul> <h4> Real-Time WebSockets </h4> <ul> <li>Live candle streaming </li> <li>AI decision logs </li> <li>Multiple concurrent sessions </li> <li>Heartbeat and reconnection handling </li> </ul> <h4> Indicator Engine </h4> <ul> <li>22+ indicators via <code>pandas-ta</code> </li> <li>Two modes: Monk (RSI + MACD) and Omni (full indicators) </li> <li>JSON-based custom indicator formulas </li> </ul> <h4> Position Management &amp; Risk Controls </h4> <ul> <li>Real-time PnL </li> <li>Auto stop-loss and take-profit </li> <li>Safety mode with -2% liquidation protection </li> <li>Leverage support </li> </ul> <h4> Certificate Generation </h4> <ul> <li>PDF certificates </li> <li>Shareable PNGs </li> <li>Verification codes </li> </ul> <h2> AI vs Traditional Algorithms </h2> <h3> Traditional Algorithms </h3> <ul> <li>Static rules </li> <li>Single trading point focus </li> <li>Rigid execution </li> </ul> <h3> AI in AlphaLabs </h3> <ul> <li>Contextual reasoning </li> <li>Multi-signal analysis </li> <li>Adaptive, flexible logic </li> </ul> <p>The AI reasons using the tickers and indicators you provide, not rigid rules.</p> <h2> Council Mode: When 4–5 LLMs Make Decisions Together </h2> <h3> How It Works </h3> <ol> <li>Query 4–5 LLMs (Claude, GPT-4, Gemini, DeepSeek, etc.). </li> <li>Each model analyzes the same input data. </li> <li>Each returns a decision and reasoning. </li> <li>Decisions are aggregated via voting or consensus. </li> <li>The system executes the council’s final decision.</li> </ol> <h3> Why It Matters </h3> <ul> <li>Diversity of thought </li> <li>Reduced bias </li> <li>Higher confidence in decisions </li> </ul> <h3> Status </h3> <ul> <li>Backtesting: Fully implemented </li> <li>Forward testing: Coming soon </li> </ul> <p>Council Mode enables comparing model reasoning and outcomes across identical data conditions.</p> <h2> Technical Highlights </h2> <h3> Backend </h3> <ul> <li>FastAPI with async/await </li> <li>PostgreSQL (Supabase) </li> <li>WebSockets </li> <li>OpenRouter </li> <li> <code>pandas-ta</code> indicators </li> </ul> <h3> Frontend </h3> <ul> <li>Next.js 16 </li> <li>React 19 </li> <li>TypeScript </li> <li>Tailwind CSS + shadcn/ui </li> <li>TradingView Lightweight Charts </li> <li>Clerk authentication </li> </ul> <h3> Features </h3> <ul> <li>Backtests </li> <li>Forward tests </li> <li>Real-time streams </li> <li>22+ indicators </li> <li>Custom formulas </li> <li>Risk controls </li> <li>Certificate generation </li> <li>Analytics </li> <li>Council Mode </li> </ul> <h2> The Kiro Advantage </h2> <p>Kiro’s specs provided:</p> <ul> <li>Structure </li> <li>Consistency </li> <li>Clear requirements </li> <li>Trackable progress </li> <li>Living documentation </li> </ul> <p>This prevented the project from becoming chaotic.</p> <h2> Challenges Overcome </h2> <h3> AI Response Consistency </h3> <p>Strict JSON validation, retries, and fallback logic.</p> <h3> Real-Time Synchronization </h3> <p>Timestamped WebSocket events and session IDs.</p> <h3> Performance </h3> <p>Precomputed indicators with caching for O(1) access.</p> <h3> Risk Management </h3> <p>Safety mode, leverage limits, automatic stop-loss.</p> <h3> Council Coordination </h3> <p>Parallel model requests and consensus algorithms.</p> <h2> What Makes This Special </h2> <ul> <li>Fully functional backtesting and forward testing </li> <li>Production-ready backend architecture </li> <li>Real-time UI </li> <li>22+ technical indicators </li> <li>Kiro-driven development workflow </li> <li>AI contextual trading </li> <li>Council Mode for multi-model intelligence </li> </ul> <h2> The Numbers </h2> <ul> <li>20+ backend services </li> <li>22+ indicators </li> <li>100+ endpoints </li> <li>Real-time WebSockets </li> <li>Multiple AI models </li> <li>4–5 model council </li> <li>Comprehensive test coverage </li> </ul> <h2> Lessons Learned </h2> <ul> <li>Specs keep complexity manageable </li> <li>Async operations are essential </li> <li>Error handling must be planned early </li> <li>Real-time systems require careful design </li> <li>AI systems need strict schemas </li> <li>AI outperforms static rules </li> <li>Councils outperform single models </li> </ul> <h2> What’s Next </h2> <ul> <li>Council Mode for live forward testing </li> <li>Multi-agent arena battles </li> <li>Social sharing and leaderboards </li> <li>Advanced analytics (Sharpe ratio, drawdown) </li> <li>Paper trading </li> <li>Mobile monitoring app </li> </ul> <h2> Final Thoughts </h2> <p>Kiro’s specs transformed a complex idea into a structured, trackable development process. AlphaLabs demonstrates the power of contextual AI reasoning over traditional trading logic. Council Mode extends this by combining multiple models for stronger decisions.</p> <p>Built for the Kiroween Hackathon 2025<br><br> Category: Frankenstein<br><br> Technologies: Kiro, FastAPI, Next.js, Supabase, WebSockets, OpenRouter</p> kiro ai webdev trading